An incremental approach to MSE-based feature selection

Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training. A neural network classifier is implemented with an incremental training approach to detect and discard irrelevant features. By learning attributes one after another, our classifier can find directly the attributes that make no contribution to classification. These attributes are marked and considered for removal. Incorporated with a Minimum Squared Error (MSE) based feature ranking scheme, four batch removal methods based on classifier error rate have been developed to discard irrelevant features. These feature selection methods reduce the computational complexity involved in searching among a large number of possible solutions significantly. Experimental results show that our feature selection methods work well on several benchmark problems compared with other feature selection methods. The selected subsets are further validated by a Constructive Backpropagation (CBP) classifier, which confirms increased classification accuracy and reduced training cost

Observation of Landau quantization and standing waves in HfSiS

Recently, HfSiS was found to be a new type of Dirac semimetal with a line of Dirac nodes in the band structure. Meanwhile, Rashba-split surface states are also pronounced in this compound. Here we report a systematic study of HfSiS by scanning tunneling microscopy/spectroscopy at low temperature and high magnetic field. The Rashba-split surface states are characterized by measuring Landau quantization and standing waves, which reveal a quasi-linear dispersive band structure. First-principles calculations based on density-functional theory are conducted and compared with the experimental results. Based on these investigations, the properties of the Rashba-split surface states and their interplay with defects and collective modes are discussed.Comment: 6 pages, 5 figure

Cohomologically hyperbolic endomorphisms of complex manifolds

We show that if a compact Kahler manifold X admits a cohomologically hyperbolic surjective endomorphism then its Kodaira dimension is non-positive. This gives an affirmative answer to a conjecture of Guedj in the holomorphic case. The main part of the paper is to determine the geometric structure and the fundamental groups (up to finite index) for those X of dimension 3.Comment: International Journal of Mathematics (to appear

Gaussian-Gamma collaborative filtering: a hierarchical Bayesian model for recommender systems

The traditional collaborative filtering (CF) suffers from two key challenges, namely, the normal assumption that it is not robust, and it is difficult to set in advance the penalty terms of the latent features. We therefore propose a hierarchical Bayesian model-based CF and the related inference algorithm. Specifically, we impose a Gaussian-Gamma prior on the ratings, and the latent features. We show the model is more robust, and the penalty terms can be adapted automatically in the inference. We use Gibbs sampler for the inference and provide a statistical explanation. We verify the performance using both synthetic and real dataset

Industry Interactions and Their Influence on Dreams, Goals, Work Interests, and Vocational Attitude of Sport Industry Job Seekers

The sport industry is filled with passionate job seekers (e.g., Todd & Andrew, 2008) who often craft lofty future work desires and idyllic dreams (Odio et al., 2014). But little is known about how these are shaped over time. Previous studies noted how plans of sport job seekers often change after encountering realistic information about the actual work in sport (Koo et al., 2016; Todd & Magnusen, 2014). This urges a closer examination of the way in which sport job seekers interact with the industry in a continuous, iterative way, and how that impacts vocational attitude and imaginations. Findings suggest that industry interaction are positively related to job seeker’s future work self, career identity, career optimism, and negatively related to career reconsideration. Implications to sport industry human resource managers are discussed

An Optimal Importance Sampling Method for a Transient Markov System

In this paper an optimal importance sampling (IS) method is derived for a transient markov system. Several propositions are presented. It is showned that the optimal IS method is unique, and it must converge to the standard Monte Carlo (MC) simulation method when the sample path length approaches infinity. Therefore, it is not the size of the state space of the Markov system, but the sample path length, that limits the efficiency of the IS method. Numerical results are presented to support the argument

Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen.

ForceGen is a template-free, non-stochastic approach for 2D to 3D structure generation and conformational elaboration for small molecules, including both non-macrocycles and macrocycles. For conformational search of non-macrocycles, ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks. These include complex peptide and peptide-like cases that can form networks of internal hydrogen bonds. By making use of new physical movements ("flips" of near-linear sub-cycles and explicit formation of hydrogen bonds), ForceGen exhibited statistically significantly better performance for overall RMS deviation from experimental coordinates than all other approaches. The algorithmic approach offers natural parallelization across multiple computing-cores. On a modest multi-core workstation, for all but the most complex macrocycles, median wall-clock times were generally under a minute in fast search mode and under 2 min using thorough search. On the most complex cases (roughly cyclic decapeptides and larger) explicit exploration of likely hydrogen bonding networks yielded marked improvements, but with calculation times increasing to several minutes and in some cases to roughly an hour for fast search. In complex cases, utilization of NMR data to constrain conformational search produces accurate conformational ensembles representative of solution state macrocycle behavior. On macrocycles of typical complexity (up to 21 rotatable macrocyclic and exocyclic bonds), design-focused macrocycle optimization can be practically supported by computational chemistry at interactive time-scales, with conformational ensemble accuracy equaling what is seen with non-macrocyclic ligands. For more complex macrocycles, inclusion of sparse biophysical data is a helpful adjunct to computation